Methods And Systems For Optimizing Messages To Users Of A Social Network

ABSTRACT

Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 13/485,784, filed May 31, 2012, which is incorporated by referencein its entirety.

FIELD OF THE INVENTION

The present application relates to social networking and, in particular,systems and methods for optimizing content and timing of messages sentto users of a social network.

BACKGROUND

Social networking websites provide a dynamic environment in whichmembers can connect to and communicate with other members. Thesewebsites may commonly provide online mechanisms allowing members tointeract within their preexisting social networks, as well as create newsocial networks. Members may include any individual or entity, such asan organization or business. Among other attributes, social networkingwebsites allow members to effectively and efficiently communicaterelevant information to their social networks.

A member of a social network may highlight or share personalinformation, news stories, relationship activities, music, and any othercontent of interest to areas of the website dedicated to the member.Other members of the social network may access the shared content bybrowsing member profiles or performing dedicated searches. Upon accessto and consideration of the content, the other members may react bytaking one or more responsive actions, such as providing an opinionabout the content, or other feedback. The ability of members to interactin this manner fosters communications among them and helps to realizethe goals of social networking websites.

In order to increase engagement of users, social networking websites maysend messages to users to encourage them to engage in activity of thesocial networking website. These messages may expressly or impliedlysuggest that the user visit the social networking website or take aparticular action in connection with the social networking website.However, if the messages are received by the user at an inopportunetime, or if the message includes content that does not interest theuser, the message may be ignored by the user, or otherwise may not beeffective in encouraging the user to use the social networking website.

SUMMARY

To increase user engagement with a social networking system, embodimentsof the invention include systems, methods, and computer readable mediato optimize messages sent to a user of a social networking system.Information about the user may be collected by the social networkingsystem. The information may be applied to train a model for determininglikelihood of a desired action by the user in response to at least onecandidate message that may be provided for the user. The socialnetworking system may provide for the user a message from the at leastone candidate message with a selected likelihood of causing the desiredaction.

In an embodiment, the information about the user includes demographicsrelated to the user, behavior of the user, and behavior of the user'sfriends. The behavior of the user can include responses, from the user,to messages sent by the social networking system. The behavior can alsoinclude dates and times of the user's activity.

In an embodiment, the model can compute scores representing likelihoodsthat each of the candidate messages will cause the desired action by theuser. The selected likelihood can be associated with a highestprobability of causing the desired action by the user. The model can becontinuously or periodically updated with changed information about theuser, including the responses by the user to messages sent by the socialnetworking system.

Many other features and embodiments of the invention will be apparentfrom the accompanying drawings and from the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a network diagram of a system for increasing engagement with asocial networking system in accordance with an embodiment of theinvention.

FIG. 2 is a block diagram of a messaging optimization module of thesocial networking system in accordance with an embodiment of theinvention.

FIG. 3 illustrates a model in accordance with an embodiment of theinvention.

FIGS. 4A-4B illustrate scores generated by a model in accordance with anembodiment of the invention.

FIG. 5 illustrates a model used to batch emails in accordance with anembodiment of the invention.

FIG. 6 shows a process for optimizing messages in accordance withembodiments of the invention.

FIG. 7 shows a diagram of a computer system in accordance with anembodiment of the invention.

The figures depict various embodiments of the present invention forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures may be employedwithout departing from the principles of the invention described herein.

DETAILED DESCRIPTION

Social Networking System—General Introduction

FIG. 1 is a network diagram of a system 100 for increasing engagement ofusers with a social networking system 130 in accordance with anembodiment of the invention. The system 100 includes one or more userdevices 110, one or more external systems 120, the social networkingsystem 130, and a network 140. For purposes of illustration, theembodiment of the system 100, shown by FIG. 1, includes a singleexternal system 120 and a single user device 110. However, in otherembodiments, the system 100 may include more user devices 110 and/ormore external systems 120. In certain embodiments, the social networkingsystem 130 is operated by a social network provider, whereas theexternal systems 120 are separate from the social networking system 130in that they may be operated by different entities. In variousembodiments, however, the social networking system 130 and the externalsystems 120 operate in conjunction to provide social networking servicesto users (or members) of the social networking system 130. In thissense, the social networking system 130 provides a platform or backbone,which other systems, such as external systems 120, may use to providesocial networking services and functionalities to users across theInternet.

The user device 110 comprises one or more computing devices that canreceive input from a user and transmit and receive data via the network140. In one embodiment, the user device 110 is a conventional computersystem executing, for example, a Microsoft Windows compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 110 can be a device having computerfunctionality, such as a smart-phone, a tablet, a personal digitalassistant (PDA), a mobile telephone, etc. The user device 110 isconfigured to communicate via the network 140. The user device 110 canexecute an application, for example, a browser application that allows auser of the user device 110 to interact with the social networkingsystem 130. In another embodiment, the user device 110 may interact withthe social networking system 130 through an application programminginterface (API) provided by the native operating system of the userdevice 110, such as iOS and ANDROID. The user device 110 is configuredto communicate with the external system 120 and the social networkingsystem 130 via the network 140, which may comprise any combination oflocal area and/or wide area networks, using wired and/or wirelesscommunication systems.

In one embodiment, the network 140 uses standard communicationstechnologies and protocols. Thus, the network 140 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network140 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 140 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 110 may display content from theexternal system 120 and/or from the social networking system 130 byprocessing a markup language document 114 received from the externalsystem 120 and from the social networking system 130 using a browserapplication 112. The markup language document 114 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 114, the browser application 112 displays the identifiedcontent using the format or presentation described by the markuplanguage document 114. For example, the markup language document 114includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 120 and the social networking system 130. In variousembodiments, the markup language document 114 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 114 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 120 andthe user device 110. The browser application 112 on the user device 110may use a JavaScript compiler to decode the markup language document114.

The markup language document 114 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 110 also includes one or more cookies116 including data indicating whether a user of the user device 110 islogged into the social networking system 130, which may enablecustomization of the data communicated from the social networking system130 to the user device 110.

The external system 120 includes one or more web servers that includeone or more web pages 122 a, 122 b, which are communicated to the userdevice 110 using the network 140. The external system 120 is separatefrom the social networking system 130. For example, the external system120 is associated with a first domain, while the social networkingsystem 130 is associated with a separate social networking domain. Webpages 122 a, 122 b, included in the external system 120, comprise markuplanguage documents 114 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 130 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure.

Users may join the social networking system 130 and then add connectionsto any number of other users of the social networking system 130 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 130 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 130. For example, in an embodiment, if users in thesocial networking system 130 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 130 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 130 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 130 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 130 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system130 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 130 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system130 provides users with the ability to take actions on various types ofitems supported by the social networking system 130. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 130 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 130, transactions that allow users to buy or sellitems via services provided by or through the social networking system130, and interactions with advertisements that a user may perform on oroff the social networking system 130. These are just a few examples ofthe items upon which a user may act on the social networking system 130,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 130 or inthe external system 120, separate from the social networking system 130,or coupled to the social networking system 130 via the network 140.

The social networking system 130 is also capable of linking a variety ofentities. For example, the social networking system 130 enables users tointeract with each other as well as external systems 120 or otherentities through an API, a web service, or other communication channels.The social networking system 130 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 130. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 130 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 130 also includes user-generated content,which enhances a user's interactions with the social networking system130. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 130. For example, a usercommunicates posts to the social networking system 130 from a userdevice 110. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 130 by a third-party. Content“items” are represented as objects in the social networking system 130.In this way, users of the social networking system 130 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 130.

The social networking system 130 includes a web server 132, an APIrequest server 134, a user account store 136, a connection store 138, anaction logger 146, an activity log 142, an authorization server 144, anda messaging optimization module 150. In an embodiment of the invention,the social networking system 130 may include additional, fewer, ordifferent components for various applications. Other components, such asnetwork interfaces, security mechanisms, load balancers, failoverservers, management and network operations consoles, and the like arenot shown so as to not obscure the details of the system.

The user account store 136 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 130. This information is storedin the user account store 136 such that each user is uniquelyidentified. The social networking system 130 also stores data describingone or more connections between different users in the connection store138. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 130 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 130, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 138.

The social networking system 130 maintains data about objects with whicha user may interact. To maintain this data, the user account store 136and the connection store 138 store instances of the corresponding typeof objects maintained by the social networking system 130. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user account store136 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 130initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 130, the social networking system 130 generatesa new instance of a user profile in the user account store 136, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 138 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 120 or connections to other entities. The connection store 138may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user account store 136 and the connection store 138 may beimplemented as a federated database.

Data stored in the connection store 138, the user account store 136, andthe activity log 142 enables the social networking system 130 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 130, user accounts of thefirst user and the second user from the user account store 136 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 138 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 130. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 130 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 130). The image may itself be represented as a node in the socialnetworking system 130. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user account store 136, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 142. By generating and maintaining thesocial graph, the social networking system 130 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 132 links the social networking system 130 to one or moreuser devices 110 and/or one or more external systems 120 via the network140. The web server 132 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 132 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system130 and one or more user devices 110. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 134 allows one or more external systems 120 anduser devices 110 to call access information from the social networkingsystem 130 by calling one or more API functions. The API request server134 may also allow external systems 120 to send information to thesocial networking system 130 by calling APIs. The external system 120,in one embodiment, sends an API request to the social networking system130 via the network 140, and the API request server 134 receives the APIrequest. The API request server 134 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 134 communicates to the external system 120via the network 140. For example, responsive to an API request, the APIrequest server 134 collects data associated with a user, such as theuser's connections that have logged into the external system 120, andcommunicates the collected data to the external system 120. In anotherembodiment, the user device 110 communicates with the social networkingsystem 130 via APIs in the same manner as external systems 120.

The action logger 146 is capable of receiving communications from theweb server 132 about user actions on and/or off the social networkingsystem 130. The action logger 146 populates the activity log 142 withinformation about user actions, enabling the social networking system130 to discover various actions taken by its users within the socialnetworking system 130 and outside of the social networking system 130.Any action that a particular user takes with respect to another node onthe social networking system 130 may be associated with each user'saccount, through information maintained in the activity log 142 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 130 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 130, the action isrecorded in the activity log 142. In one embodiment, the socialnetworking system 130 maintains the activity log 142 as a database ofentries. When an action is taken within the social networking system130, an entry for the action is added to the activity log 142. Theactivity log 142 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 130,such as an external system 120 that is separate from the socialnetworking system 130. For example, the action logger 146 may receivedata describing a user's interaction with an external system 120 fromthe web server 132. In this example, the external system 120 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system120 include a user expressing an interest in an external system 120 oranother entity, a user posting a comment to the social networking system130 that discusses an external system 120 or a web page 122 a within theexternal system 120, a user posting to the social networking system 130a Uniform Resource Locator (URL) or other identifier associated with anexternal system 120, a user attending an event associated with anexternal system 120, or any other action by a user that is related to anexternal system 120. Thus, the activity log 142 may include actionsdescribing interactions between a user of the social networking system130 and an external system 120 that is separate from the socialnetworking system 130.

The authorization server 144 enforces one or more privacy settings ofthe users of the social networking system 130. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 120, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems120. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 120 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 120 toaccess the user's work information, but specify a list of externalsystems 120 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 120 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 144 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 120, and/or other applications and entities. Theexternal system 120 may need authorization from the authorization server144 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 144 determines if another user, the external system120, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

The social networking system 130 may also include the messagingoptimization module 150 for optimizing messages (also referred to ascommunications) sent to a user. Messages can be emails, SMS, texts,instant messages, chat messages, or any type of message that can be sentto the user. Such messages can include a digest of information. In otherwords, the social networking system 130 can accumulate information andactivity over time that may be of interest to a user, and include asummary of such information in a message sent to the user. Messages canalso include notifications. A notification may be an email or other typeof message that is sent to a user upon the occurrence of an event.Notifications can be used, in some circumstances, to inform a user of anevent immediately or soon after the event takes place.

Messages (also referred to as communications) sent to the user by thesocial networking system 130 may generally be used to encourage orentice the user to increase engagement with the social networking system130, or to encourage the user to take a particular action or series ofactions in connection with the social networking system 130. Forexample, notification messages may inform the user of comments andfeedback from other users regarding content posted by the user. Thefeedback may encourage the user to revisit the social networking system130 to see what other users and friends have said about the user'scontent. Although feedback and comments have been used as an example ofinformation that can be included in messages, other information can beincluded as well. For example, messages can contain lists of friends,lists of potential friends, activity of friends, group invitations,event invitations, etc. Messages containing such information can also besent to the user to encourage other reactions from the user. Forexample, messages can be sent to the user to invite the user to join orparticipate in a group, event, or discussion. Messages can also be sentto encourage the user to connect to additional friends, to viewinteresting content, to play social network-related games, etc. Ingeneral, messages can be sent to the user to encourage the user toparticipate, or increase participation, in any aspect of the socialnetworking system 130.

In order to encourage the user to react to the messages, it may bedesirable for the social networking system 130 to optimize messages sentto the user. For example, if a user enjoys viewing a particular type ofcontent, messages that include that type of content, or inform the userthat the type of content is available on the social networking system130, may be more effective at encouraging the user to log into and/orengage with the social networking system 130. Similarly, if a usertypically logs into the social networking system 130 at a particulartime, e.g., a particular time of day and/or a particular day of theweek, then messages sent to the user at a specified time may be moreeffective at encouraging the user to log into the social networkingsystem 130. In some cases, a message sent to the user a few hours (e.g.,3 hours) before the user usually logs into the social networking system130 can increase the likelihood that the user will log into the socialnetworking system 130 at the usual time. Accordingly, the socialnetworking system 130 may include messaging optimization module 150 tooptimize the content included in a message and the time the message issent, to encourage users to log into and/or engage in activity on thesocial networking system 130.

Messaging Optimization

FIG. 2 is a diagram of the messaging optimization module 150 of FIG. 1in accordance with an embodiment of the invention. The messagingoptimization module 150 can implement a machine learning scheme. Inother words, the messaging optimization module 150 can be trained, basedon user responses to messages, to optimize the messages sent to theuser. In order to implement machine learning, the messaging optimizationmodule 150 may monitor and record information about a user in order tocreate a profile of the user. The profile of a user includes informationsuch as demographics of the user, as well as actions and behavior of theuser and friends of the user on the social networking system 130. Themessaging optimization module 150 can also create a model of the userthat can be used to anticipate or predict whether a message sent at aparticular time, and a message that includes particular content, will beeffective in eliciting a desired response or reaction from the user. Thedesired response may include any action or actions, such as clicking ona link in the message, logging into the social networking system 130,joining a group, responding to a friend request, sending a friendinvitation, providing feedback, viewing content on the social networkingsystem 130, commenting on content posted within the social networkingsystem 130, etc. The prediction can be a score, ranking, or probabilitythat the message will cause the desired response from the user. By usingthe prediction, the messaging optimization module 150 can optimize thetime the messages are sent, and the content contained in the messages,to increase the effectiveness of the messages in producing the desiredresponse or reaction from the user.

The messaging optimization module 150 includes an email engine 202, amessage transfer agent (MTA) 204, a multifeed module 206, a coefficientmodule 208, a message log 210, a login log 212, a user profile module214, and a user modeling module 216. In an embodiment of the invention,the functions performed by the components (e.g., logs, engines, modules,etc.) shown in FIGS. 1 and 2 may be variously replaced by, combinedwith, or integrated into other components. The social networking system130 may include additional, fewer, or different components for variousapplications. Other components such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The email engine 202 controls the provision of messages through themessage transfer agent (MTA) 204 to a user of the social networkingsystem 130 based on information exchanged with the multifeed module 206,the coefficient module 208, the message log 210, and the login log 212.The user profile module 214 and the user modeling module 216 can act tooptimize the content and/or timing of messages sent to the user. In anembodiment, the message transfer agent (MTA) 204 may provide messages toa user who receives such messages through, for example, email, SMS, or amessage system external to the social networking system 130, such as theexternal system 120.

The coefficient module 208 may be used in identifying particular friendsof the user whose activities may be considered in determining the timingand content of messages sent by the social networking system 130 to theuser. The coefficient module 208 can provide coefficients as measures ofrelatedness between nodes in the social graph of the social networkingsystem 130. For example, coefficients may indicate that a user is closerto her best friend than to another person befriended by the user. Insome instances, coefficients may provide, or use, weights for edgesbetween nodes in the social graph to determine relatedness. For example,a greater weight may be given to a particular friend of the user if thatfriend and the user have a high number of common friends, to aparticular friend of the user if that friend frequently comments on thestatus of the user or of another friend of the user. The coefficientmodule 208 may store raw features about interactions between nodes, andhas trained algorithms based thereon to provide general purposefunctions to provide rankings, intelligence, suggestions, andrecommendations.

The message log 210 maintains information about messages provided by thesocial networking system 130 to the user. The message log 210 mayinclude transactional information, such as the category and type ofmessages sent to the user, as well as the activities that prompt ortrigger provision of the notifications. The message log 210 also mayinclude information regarding the date and time the messages areprovided to the user. In an embodiment of the invention, the message log210 may be implemented as a database table.

The multifeed module 206 may provide other types of information aboutthe social network of a user. The multifeed module 206 may manage andtrack information about activities of friends and other contacts of theuser. For example, the multifeed module 206 may provide informationabout status information posted by friends. Status information mayinclude temporary information published by a friend of the user thatreflects the current thoughts and expressions of the friend. By itsnature, posted status information may change frequently and themultifeed module 206 tracks such changes. As another example, themultifeed module 206 may also track information about photos and videosthat are posted by friends of the user.

The multifeed module 206 may also track stories concerning friends ofthe user. Stories may include various actions taken by the friends inthe social network. For example, a story may involve one or more friends“liking” the photo of another friend. As another example, a story mayinvolve one or more friends “liking” the status of another friend. Inaddition to the information described herein, other types of informationalso may be tracked by the multifeed module 206.

The login log 212 maintains information regarding the history ofengagement by a user with the social networking system 130. Theengagement information maintained by the login log 212 may includeinformation regarding the date and times of prior visits by the user tothe social networking system 130. The engagement information may alsoinclude the date and time of the last visit by the user to the socialnetworking system 130. In an embodiment of the invention, the login log212 may maintain other types of engagement information, such as thehistory of logins to the social networking system 130 by the user or thehistory of certain acts or actions taken by the user.

The email engine 202 may compare information from the login log 212 andthe user modeling module 216, with one or more predetermined timeintervals to determine whether to provide a message to the user. Inparticular, the email engine 202 may retrieve from the login log 212 thedate and time of the last engagement of the user with the socialnetworking system 130, and/or the date and time of the last email sentto the user by the email engine 202. Email engine 202 may also retrievefrom the user modeling module 216 an optimized time to send a subsequentemail or other type of message. If the time since the last engagementand/or the time since the last email is more than one or morepredetermined time intervals, the email engine 202 causes the message tobe provided to the user. In some cases, the email engine 202 will causethe message to be provided to the user at the optimized time retrievedfrom the user modeling module 216.

The user profile module 214 may identify, monitor, and record featuressuch as the behavior of the user, demographics of the user, and behaviorof friends of the user. These features, as well as other informationabout a user, can be monitored and recorded in order to create a profileabout the user and how she uses the social networking system 130. Theprofile can then be used to model the user's behavior, and predict howthe user will react to messages or other stimulus provided by the socialnetworking system 130.

The behavior monitored by the user profile module 214 can include thetime that a user is active, or most active, on the social networkingsystem 130, including, for example, the time of day, the day of theweek, and the day of the month that the user is active (or most active)on the social networking system 130. For example, if a user typicallylogs in each Saturday night at or around 7:00 PM, but rarely or neverlogs in on Monday mornings at 10:00 AM, the user profile module 214 canmonitor and record such activity.

The behavior monitored and recorded by the user profile module 214 canalso include the type of activity in which the user engages. Forexample, the user profile module 214 can monitor whether a user playsgames, interacts with friends, posts content, listens to music, joins agroup, reads articles, comments on other users' content, or engages inother types of activity on the social networking system 130. The timingof the user's behavior also may be recorded by the user profile module214.

In an embodiment, the behavior recorded by the user profile module 214can include the user's reactions to messages sent to the user. As anexample, the user profile module 214 can record, after the user receivesa message such as an email from the social networking system 130,whether the user clicks a particular link among many links included inthe email. The links within the email can, for example, present awebpage of the social networking system 130 or cause the user to takesome other action desired by the social networking system 130. The userprofile module 214 can also record whether, after the user has receiveda message, the user has taken no action with respect to the message. Theuser profile module 214 can also monitor the types of messages, and thetimes that the messages were sent, that result in the greatestprobability of responses from the user. For example, a user may respondmore often to emails that contain information about the user's friends'status updates, and may respond less often to emails that containinformation about pictures posted on the social networking system 130.The user profile module 214 can record such information for use in theuser's profile. As another example, a user may respond more often toemails that are sent at 10:00 AM, and less often to emails that are sentat 8:00 PM. Again, the user profile module 214 can record suchinformation in the user's profile, so that it can be used to predict howa user will respond to various types of messages. By incorporating theuser responses to messages into the user's profile, the socialnetworking system 130 can learn to optimize messages sent to the user.

In order to identify and record user behavior, the user profile module214 may monitor and record user activity over a period of time. Theperiod of time may be based upon a desired period or frequency ofactivity. For example, in order to determine whether a user typicallylogs in on Saturday nights, the user profile module 214 may monitor,record, and compile the usage habits and behavior of the user over aperiod of, for example, 100 days, or a longer or shorter period of time.In this way, the user profile module 214 can monitor activity on anumber of different Saturday nights to determine if the user typicallylogs in on Saturday nights. As another example, in order to determinethe time of day at which a user is most active on the social networkingsystem 130, the user profile module 214 may monitor, record, and compilethe usage habits and behavior of the user over, for example, a period ofdays. In this way, the user profile module 214 can record the user'sactivity over a period of days to determine what time of day the user istypically the most active on the social networking system 130. The userprofile module 214 can also monitor the amount of user activity during aperiod of time. For example, the user profile module 214 can monitor howmany days a user was active, and how many days a user was inactive, overa predetermined time period (e.g., a 100 day period). As anotherexample, the user profile module 214 can monitor the extent of theactivities of the user over a predetermined period of time. Theforegoing sample activities and periods of time are provided forillustration.

The user profile module 214 can monitor and record data about users forany desired type or extent of activity over any desired period of time,such as any suitable number of days, weeks, months, years, etc. Thedesired period of time may allow the social networking system 130 tomaintain a profile of the user that reflects current usage by the userof the social networking system 130. Information about messages, and theuser's reactions to messages, can also be monitored and recorded by theuser profile module 214. The user profile module 214 may monitor thedate and time of the last message sent to the user, the total number ofmessages sent to the user, the type and content of messages sent to theuser, the number of invitations to join a group that have been sent tothe user, etc. For example, if the user was sent a notification onFriday evening that another user liked her status, the user profilemodule 214 can record such information and include it as part of theuser's profile. As another example, if a message sent to the userprovided links for the user to see news stories, friend requests, ornotifications from other users in the social networking system 130, theuser profile module 214 can record which links the user selected.

The user profile module 214 can also maintain and monitor demographicsabout a user. For example, demographics may include, but are not limitedto, birthplace, birth date, hometown, current town, locale, time zone,country of residence or citizenship, age, race, weight, height, maritalstatus, or gender of a user. Demographics can also include income level,type of job, educational achievements such as degrees earned and schoolsattended, etc. These types of information may, in some instances, beused by the social networking system 130 to create an accurate profileof the user.

Demographics can also include attributes and information about theuser's account on the social networking system 130. For example, theuser profile module 214 can monitor the number of friends of the user,the number of outstanding messages or notifications that the user hasread or has not read, the number of outstanding friend requests receivedor sent by the user, etc.

The activity of a user's friends can also constitute a part of theuser's profile. As such, the user profile module 214 can monitor andrecord information about the user's friends. For example, if a user'sfriends log onto the social networking system 130, or are otherwiseactive on the social networking system 130 at a particular time, or on aparticular day of the week, the user profile module 214 can include thatinformation in the user's profile. Similarly, if the user's friendsengage in particular activities on the social networking system 130(e.g., playing games, viewing content, etc.), or if the user's friendspost or like particular content, the social networking system 130 caninclude such information in the user's profile. The dates and times ofthe activities of the user's friends can also be monitored and recorded.In general, the user profile module 214 can monitor and include anyactivity of a user's friends in the user profile so as to create a moreaccurate profile of the user.

The user profile module 214 can continuously or periodically monitor andrecord user and friend activity and demographics so that the userprofile module 214 can update the user profile over time, as theactivities and demographics change. For example, a particular user maylog in most often on Saturday nights. Say, for example, that the user'swork schedule changes so that it is no longer convenient for the user tolog in on Saturday nights. The user may stop logging in as frequently onSaturday nights, and may begin logging in more often on Monday nights.In this example, the user profile module 214 can continuously orperiodically monitor the user's login habits and behavior over time todetermine that the user's habits and behavior have changed from loggingin on Saturday nights to logging in on Monday nights.

In an embodiment, the user profile module 214 can receive and monitorinformation about the user by retrieving such information from the useraccount store 136. As described above, the user account store 136 canmaintain information, including demographics and activity, related to auser account.

By collecting and compiling activity and demographics relating to auser, the user profile module 214 can create a user profile thatestimates or approximates the preferences of the user. The user profilecan be used to predict how a user will respond to messages based ontheir content and timing, as well as the likelihood that the user willtake desired action in response to such messages.

The user modeling module 216 may use the information collected by theuser profile module 214 (e.g., the user profile) to create a model of auser. The model may be, for example, a software program, softwarefunction, algorithm, or any other suitable functionality or tool thatcan be used to predict behavior of the user. In an embodiment, the modelcan provide a prediction (e.g., a score or a probability) of how theuser will react to messages sent by the social networking system 130.

The user modeling module 216 can incorporate any information about theuser collected by the user profile module 214 into the model. Asdiscussed above, such information can include, but is not limited to,the activity of the user on the social networking system 130, theactivity of the user's friends on the social networking system 130, anddemographics about the user.

Known reactions of a user can also be incorporated into the model. Forexample, the data collected in a user profile may show that, if a userfirst registered with the social networking system 130 or typically logsinto the social networking system 130 at a particular time of the day,the user is even more likely to log in at that time if the socialnetworking system 130 sends a message to the user at a predeterminedduration of time beforehand, such as three hours or any other suitableduration of time. The user profile module 214 may monitor the number oftimes the user logs in after the message is sent. Then, the usermodeling module 216 can compare that number with the number of times theuser logs in if no message is sent or if a message is sent at varyingtimes prior to the user logging in. The user modeling module 216 canalso compare the number of times the user logs in when the message issent three hours before a typical login time, five hours before atypical login time, or any predetermined time period before the user'stypical login time.

The user modeling module 216 can also compare the reaction of the userto various types of content sent in an email. For example, the usermodeling module 216 can compare whether the user more or less frequentlyclicks on a link to the social networking system 130, or logs into thesocial networking system 130 based on whether the message containsstories about friends of the user, content posted by friends of theuser, news articles commented on by friends of the user, etc.

If the message is a digest, the user modeling module 216 can record thetype of content the user clicked within the digest. Digests are messagessent to the user that can include compilations or summaries of differenttypes of content and activity that has taken place on the socialnetworking system 130. In an embodiment, the content of the digest canbe organized into different sections, each section containing adifferent summary. For example, sections in a digest can include a listof friends, a summary of popular stories, a summary of friend requests,a summary of notifications, etc. In an embodiment, the sections andsummaries included in a digest can vary from message to message.

User reactions to a digest, including the section within the digest thatinvoked the user reaction, can be recorded and incorporated by the usermodeling module 216 into the user model. For example, if a user clickson a summary of popular stories within the digest, the social networkingsystem 130 can record the fact that the user clicked on the sectionwithin the digest related to popular stories. Such information, whenincorporated into the user model, can indicate that the user has anincreased likelihood of reacting to summaries of popular stories.Similarly, the user clicking on other sections within the digest can beincorporated into the model to indicate that the user has a preferencefor those other sections that were clicked.

The user modeling module 216 can use the recorded information to createa model that predicts how the user may react to the timing and contentof new messages. In an embodiment, the user modeling module 216 maycreate a model per user, i.e., a model for one user of the socialnetworking system 130 or for every user of the social networking system130. The model may be used to predict how a user will react to thetiming and content of messages from the social networking system 130.

In another embodiment, the user modeling module 216 may create a modelfor a type of user. Such a model may be used to predict reactions ofmultiple users who fall into a particular class or category. Forexample, the user modeling module 216 may create a model that representsthe typical behavior of users who reside on the west coast of the UnitedStates. As another example, the user modeling module 216 may create amodel for users between 20 and 30 years old who listen to a certaingenre of music. Any other category of user may also be used to createsuch a model. A model that represents a type of user may be useful topredict how a particular population of users will react to messages fromthe social networking system 130 based on their timing and content.

Once a model representing a user is created, the user modeling module216 may modify and update the model based upon changes to the user'sactivity, the user's demographics, and activity of the user's friends onthe social networking system 130. For example, if a user typically logsinto the social networking system 130 on Saturday evenings at 5:00 PM,the user profile may reflect this activity. However, if the user'sactivity changes so that the user stops logging into the socialnetworking system 130 on Saturday evenings at 5:00 PM, and beginslogging into the social networking system more often on Sundayafternoons at 2:00 PM, then the user modeling module 216 can adapt ormodify the model for that user to reflect the user's new activity,habits and behavior. Although the example above uses login time of theuser, the user modeling module 216 can adapt or update the model basedon any change to user activity, user friend activity, or userdemographics.

FIG. 3 shows an example of a model 302 that may be generated by the usermodeling module 216. The model 302 can be based upon a user profile 304,which can include features such as user demographics 306, user activity308, and activity of friends of the user 310. The user profile 304 canalso include other information, as discussed above, that can be used bythe model 302 to predict user reactions.

The model 302 may receive various inputs 314. In an embodiment, theinputs 314 can represent various attributes of, or related to, a messagethat may influence whether the user will react to the message as desiredby the social networking system 130. For example, it may be useful todetermine if and how a user will react to a message sent by the socialnetworking system 130 at a particular hour of the day and on aparticular day of the week. In this example, inputs representing an hourof the day 318 or inputs representing the day of the a week 320 may beprovided to the model 302. An output 316 provided by the model 302 mayrepresent a prediction (e.g., a probability) that the user will react toa message sent at a particular time of day or day of the week.

As another example, it may be useful to determine if and how a user willreact to particular types of content within a message sent to a user. Inthis case, data representing types of content 322 to be included in amessage can be provided to the model 302 as inputs. The model 302 maythen use the information from the user profile 304 to produce an output316 that represents the likelihood that the user will react to the typesof content 322.

The inputs 314 can also be combined, as desired, so that the output 316can provide information about how the user will react to variouscombinations of message attributes. For example, it may be desirable todetermine what time the message should be sent, what day of the week theemail should be sent, and what content the message should contain sothat the message has the highest likelihood of eliciting a response fromthe user. Accordingly, in this example, the input 314 may include thehour of the day 318, the day of the week 320, and the types of content322. The model 302 can produce the output 316 that includes thelikelihood of the user responding to various combinations of the inputs314. For example, the output 316 may show that a particular user is mostlikely to respond to messages containing friend requests that are sentbetween 3:00 PM and 4:00 PM in the afternoon on Sundays. As anotherexample, the output 316 may show that a type of user is most likely toprovide a desired response to an email when the email is sent before10:00 PM on weekday evenings and contains news stories of familymembers.

In an embodiment, any suitable inputs in addition to the hour of the day318, the day of the week 320, and the types of content 322 may beincluded in the inputs 314 provided to the model 302. Such inputs mayinclude, but are not limited to, games the user can play, other usersthat the user can befriend, groups the user can join, articles that theuser can read, etc. The inputs may be provided to the model individuallyor in any suitable combinations.

In an embodiment, the output 316 may provide a score or scores thatrepresent the likelihood that a user will react to a particular input314. FIG. 4A shows an example of the output 316 that includes scores. Inthis example, the social networking system 130 may be preparing to senda message to the user based on the model 302. In order to optimize theeffectiveness of the message, the social networking system 130 may usethe model 302 to determine on which day the message should be sent.Accordingly, the day of the week 320 may be provided as an input to themodel 302. As shown in FIG. 4A, the output 316 of the model 302 mayprovide a score for a candidate message that could potentially be senton each day of the week. In FIG. 4A, the score for each candidatemessage corresponding to each day is shown in the bottom row, just beloweach day of the week. As shown, the model 302 for this particular userproduced, for the candidate messages, a score of 5% for Monday, 11% forTuesday, 5% for Wednesday, 5% for Thursday, 11% for Friday, 26% forSaturday, and 37% for Sunday. These scores may represent the likelihoodthat the user modeled by the model 302 will react, as desired, to themessage, based on which day the message is sent. Since the score forSunday is the highest, the model may predict that the message should besent on Sunday in order to have the highest likelihood of inducing adesired reaction from the user, such as clicking on a link in themessage, logging onto the social networking system 130, joining a grouplisted in the message, etc. In some instances, it may be desirable tosend a message to the user that will not invoke a response. In thiscase, the social networking system 130 can send the message to the useron Monday, Wednesday, or any time the probability of invoking a responsefrom the user is relatively low.

In an embodiment, the score produced by the model 302 may be a relativescore. In other words, the model 302 may provide a relative score orrank that shows which input has the highest (or lowest) likelihood ofcausing a response, as compared to the likelihood of the other inputs.For example, in FIG. 4A, Sunday may have the highest relative score,with respect to the other days that were provided as inputs to the model302. In another embodiment, the score produced by the model 302 may bean absolute score or probability. In other words, the model 302 mayproduce a score of 50%, for example, to indicate a 50% probability thata user will react to a particular message, without providing anycomparison or ranking with respect to scores for other inputs.

FIG. 4B shows another example of scores, corresponding to candidatemessages that potentially may be sent to a user, that can be determinedby the model 302. In FIG. 4B the output 316 includes scores based oncombinations of two variables provided as inputs to the model 302. Forexample, a top row 402 shows one input as the type of content to beincluded in a message. A middle row 404 shows a second input as the dayof the week on which to send the message. A third row 406 shows thescores produced by the model 302. The scores are associated with eachcombination of input. For example, the score in box 408 shows alikelihood that the user will respond to messages sent on Monday (fromrow 404) that include a list of friend requests (from row 402). Box 410shows a likelihood that a user will respond to messages sent on Tuesdaythat include a list of friend requests. Similarly, box 412 shows alikelihood that a user will respond to messages sent on Monday thatinclude a summary of popular stories, and box 414 shows a likelihoodthat a user will respond to messages sent on Tuesday that include asummary of popular stories. In this example, box 416 indicates that, ofall the combinations of inputs in rows 402 and 404, the user has thehighest likelihood of responding to messages sent on Sundays thatinclude summaries of popular stories.

Although FIG. 4B shows scores for a combination of two inputs, the model302 can produce scores for any number of inputs, combined in anysuitable combinations. For example, if the inputs of the day of the weekand the hour of the day are combined, the model 302 can produce scoresthat represent the likelihood that a user will respond to candidatemessages for each hour of the week. The social networking system 130 mayuse such scores to determine the optimal hour of the week to send amessage to a particular user. Any combination of any inputs can be usedso that the model 302 can produce scores for optimizing messages sent toa user.

Additionally, although the scores are shown in FIGS. 4A and 4B aspercentages, it is understood that the model 302 can provide scores andrank the likelihood of the user reacting to various inputs in anyappropriate way. For example, the scores may be expressed as integers,real numbers, fractions, or any other suitable numerical or quantitativeforms. As another example, the scores may be expressed in non-numericalor qualitative forms (e.g., “very likely”, “not likely”, “moreprobable”, “less probable”, etc). Also, although the scores are shownwith respect to days of the week, the scores may also be used to scoreand rank hours of the day, hours of the week, days of the month, monthsof the year, the type of content to be included in the messages, anyother information relating to the message, or any combination thereof.

FIG. 5 shows an example of how the model 302 may be used within thesocial networking system 130. As described above, the social networkingsystem 130 may periodically send emails to users of the socialnetworking system 130. The emails may be tailored for and provided toeach user on a dedicated basis. In an embodiment, the social networkingsystem 130 may create an email cache 504 that contains emails to be sentto users of the social networking system 130. In another embodiment, theemails need not be cached. In order to optimize what time to send theemails, and what content to include in the emails, the social networkingsystem 130 may create models for each user of the social networkingsystem 130, such as the model 302. The model 302 can be used to predictthe best time to send the email and the best content to include in theemail in order to increase the likelihood that the user will respond orreact to the email as desired. These models can be stored, for example,in a model repository 502. For each email, the social networking system130 can use a model from the model repository 502 that can predict theresponse of the recipient to the email based on the timing and contentof the email. The emails can be tailored to include the content mostlikely to elicit a desired response from that particular recipient. Theemails can be individually sent to each user on a dedicated basis, asdiscussed above, or sorted or batched so that the emails are sent out ingroups on the date and time most likely to elicit a response or reactionfrom each user. An email batch 506 can include batches for emails thatare to be sent out on different days, batches for different hours of theday, batches for different hours of the week, batches for different daysof the month, etc. As shown in FIG. 5, the emails may be batchedaccording to the day they are to be sent and according to the hour ofthe day they are to be sent. For example, the emails to be sent in abatch 508 on July 13 between 1:00 AM and 2:00 AM may be batched in abucket 510. The social networking system 130 can then process thebatched emails and send them to the recipients at the scheduled date andtime.

Referring again to FIG. 2, the user profile module 214 and the usermodeling module 216 can also use the reactions of users to the messagesto train the model 302 in connection with machine learning. As notedabove, the user profile module 214 can monitor and record user responsesto messages sent by the social networking system 130. These responsescan include clicking a link in the message, logging onto the socialnetworking system 130, joining a group, etc. Such responses can beincluded in a user profile and used to tailor or train the model 302 toprovide more accurate predictions of user responses. For example, assumethat the average user is most likely to respond to an email sent threehours before the time she usually logs into the social networking system130. However, assume further that a particular user (User A) differsfrom the average user. In User A's case, User A is more likely torespond to an email sent six hours before she usually logs into thesocial networking system 130. In this case, the user profile module 214can record User A's response to messages sent three hours, and sixhours, prior to User A's usual login time, and include those responsesin the profile. The profile can then reflect User A's preference forresponding as desired to emails that are sent six hours prior to herlogin time. This information can be incorporated into the model 302 forUser A by user modeling module 216. The output 316 and scores producedby the model 302 can then reflect the higher likelihood of User A torespond to messages sent six hours prior to User A's login time. Byincorporating user responses into the user profile and the model 302,the social networking system 130 can continuously tailor and update eachmodel 302 to more accurately reflect the likelihood that a user willrespond to messages sent by the social networking system 130.

FIG. 6 shows an example process 600 for optimizing messages inaccordance with an embodiment of the invention. In block 602,information about a user of a social networking system is collected. Inblock 604, the information is used to train a machine learning model fordetermining likelihood of a desired action by the user in response tocandidate messages. In block 606, scores representing likelihoods thateach of the candidate messages will cause the desired action by the userare computed. In block 608, a message associated with a selectedlikelihood of causing the desired action is provided. In block 610, themessage is scheduled in a batch based on a day of the week and a timeinterval during which the message is to be sent. In block 612, themachine learning model is updated with changed information about theuser. In block 614, the machine learning model is updated with actiontaken by the user in response to the message. In an embodiment, afterblock 614, the process 600 can continue to any of blocks 602, 604, 606,608, 610, 612, or 614.

In an embodiment of the invention, the process 600 may be entirely orpartially performed by the messaging optimization module 150. In anembodiment of the invention, the process 600 may be performed at leastin part by the social networking system 130.

CONCLUSION

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 7 illustrates anexample of a computer system 700 that may be used to implement one ormore of the computing devices identified above. The computer system 700includes sets of instructions for causing the computer system 700 toperform the processes and features discussed herein. The computer system700 may be connected (e.g., networked) to other machines. In a networkeddeployment, the computer system 700 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 700 may be the social networking system 130, the user device 110,the external system 120, or a component thereof. In an embodiment of theinvention, the computer system 700 may be one server among many thatconstitutes all or part of the social networking system 130.

The computer system 700 includes a processor 702, a cache memory 704,and one or more executable modules and drivers, stored on acomputer-readable medium, directed to the processes and featuresdescribed herein. Additionally, the computer system 700 includes a highperformance input/output (I/O) bus 706 and a standard I/O bus 708. Ahost bridge 710 couples the processor 702 to the high performance I/Obus 706, whereas I/O bus bridge 712 couples the two buses 706 and 708 toeach other. A system memory 714 and one or more network interfaces 716couple to the bus 706. The computer system 700 may further include videomemory and a display device coupled to the video memory (not shown).Mass storage 718 and I/O ports 720 couple to the bus 708. The computersystem 700 may optionally include a keyboard and pointing device, adisplay device, or other input/output devices (not shown) coupled to thebus 708. Collectively, these elements are intended to represent a broadcategory of computer hardware systems, including but not limited tocomputer systems based on the x86-compatible processors manufactured byIntel Corporation of Santa Clara, Calif., and the x86-compatibleprocessors manufactured by Advanced Micro Devices (AMD), Inc., ofSunnyvale, Calif., as well as any other suitable processor.

An operating system manages and controls the operation of the computersystem 700, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System; the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif.; UNIXoperating systems; Microsoft® Windows® operating systems; BSD operatingsystems; and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detailbelow. In particular, the network interface 716 provides communicationbetween the computer system 700 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 718 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 714 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor702. The I/O ports 720 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures,and various components of the computer system 700 may be rearranged. Forexample, the cache 704 may be on-chip with processor 702. Alternatively,the cache 704 and the processor 702 may be packed together as a“processor module”, with processor 702 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 708 may couple to thehigh performance I/O bus 706. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 700being coupled to the single bus. Furthermore, the computer system 700may include additional components, such as additional processors,storage devices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 700 which, when read and executed by one or moreprocessors, cause the computer system 700 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system700, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 702.Initially, the series of instructions may be stored on a storage device,such as the mass storage 718. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 716. The instructions are copied from thestorage device, such as the mass storage 718, into the system memory714, and then accessed and executed by processor 702.

Examples of computer readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system700 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “another embodiment”, or the like means that aparticular feature, design, structure, or characteristic described inconnection with the embodiment is included in at least one embodiment ofthe disclosure. The appearances of, for example, the phrase “in oneembodiment”, “in an embodiment”, or “in another embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Moreover, whether or not there isexpress reference to an “embodiment” or the like, various features aredescribed, which may be variously combined and included in someembodiments but also variously omitted in other embodiments. Similarly,various features are described which may be preferences or requirementsfor some embodiments but not other embodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: logging one or more useractivities associated with a user of an online system, the useractivities performed on the online system and including the user'sresponses to one or more messages sent to the user by the online system;accessing a message response prediction model for the user, the messageresponse prediction model outputting a prediction, based on the loggeduser activities, of whether the user's response to one or more candidatemessages will elicit a specified user response; for each of the one ormore candidate messages: identifying one or more attributes of thecandidate message associated with the specified user response, and basedat least in part on the identified one or more attributes, using themessage response prediction model to determine a likelihood that theuser will have the specified user response to the candidate message; andselecting, by the processor, a candidate message to send to the userbased on the determined likelihoods of the one or more candidatemessages.
 2. The method of claim 1, wherein user activities associatedwith a user of an online system further include activities performed onthe online system by other users connected to the user.
 3. The method ofclaim 1, further comprising: maintaining a user profile includingcharacteristics of the user; and wherein the message prediction modeluses characteristics of the user to output the prediction of whether theuser's response to the one or more candidate messages will elicit thespecified user response.
 4. The method of claim 3, whereincharacteristics of the user includes at least one of user demographics,behavior of the user, and behavior of friends of the user.
 5. The methodof claim 4, wherein the behavior of the user includes at least one ofdate and time of activities of the user, types of activities of theuser, extent of activities of the user, and responses of the user toprevious messages provided by the online system.
 6. The method of claim4, wherein the behavior of friends of the user includes at least one ofdate and time of activities of the friends, types of activities of thefriends, and extent of activities of the friends.
 7. The method of claim1, characteristics of the user are based on user activities thatoccurred during a selected period of time.
 8. The method of claim 1,wherein attributes of the candidate message comprise a type of contentto be included in the message.
 9. The method of claim 1, furthercomprising determining a likelihood that the user will have thespecified user response to the candidate message based at least in parton at least one of a day of the week the message is to be sent, and atime the message is to be sent by the online system to the user.
 10. Themethod of claim 1, wherein the selected candidate message is associatedwith a highest determined likelihood that the user will perform thespecified response to receiving selected candidate message.
 11. Themethod of claim 1, further comprising updating the message responseprediction model for the user with changed information about the user.12. The method of claim 11, wherein the updating is performedperiodically during a selected interval.
 13. The method of claim 11,wherein the updating is performed continuously during a selectedinterval.
 14. The method of claim 1, further comprising updating themessage response prediction model for the user with action taken by theuser in response to the message.
 15. The method of claim 1, furthercomprising associating each of a plurality of message predictions modelswith each of a plurality of users of the online system.
 16. The methodof claim 1, further comprising generating a message response predictionmodel for a group of users, including the user, of the online system.17. The method of claim 1, further comprising including the selectedcandidate message in a group of messages for delivery to the user basedat least in part on a day of the week and a time interval during whichthe selected candidate message is to be sent.
 18. The method of claim 1,further comprising training the message response prediction model usinginformation from the user profile and response information about theuser's responses to messages sent by the online system.
 19. Anon-transitory computer storage medium storing computer-executableinstructions that, when executed by a processor, cause a computer systemto: log one or more user activities associated with a user of an onlinesystem, the user activities performed on the online system and includingthe user's responses to one or more messages sent to the user by theonline system; access a message response prediction model for the user,the message response prediction model outputting a prediction, based onthe logged user activities, of whether the user's response to one ormore candidate messages will elicit a specified user response; for eachof the one or more candidate messages: identify one or more attributesof the candidate message associated with the specified user response,and based at least in part on the identified one or more attributes, usethe message response prediction model to determine a likelihood that theuser will have the specified user response to the candidate message; andselect, by the processor, a candidate message to send to the user basedon the determined likelihoods of the one or more candidate messages. 20.A system comprising: at least one processor; and a memory storinginstructions configured to instruct the at least one processor to: logone or more user activities associated with a user of an online system,the user activities performed on the online system and including theuser's responses to one or more messages sent to the user by the onlinesystem; access a message response prediction model for the user, themessage response prediction model outputting a prediction, based on thelogged user activities, of whether the user's response to one or morecandidate messages will elicit a specified user response; for each ofthe one or more candidate messages: identify one or more attributes ofthe candidate message associated with the specified user response, andbased at least in part on the identified one or more attributes, use themessage response prediction model to determine a likelihood that theuser will have the specified user response to the candidate message; andselect, by the processor, a candidate message to send to the user basedon the determined likelihoods of the one or more candidate messages.